fix(`SparseTensor.__getitem__`): support `np.ndarray` and fix `List[b… (#194)

* fix(`SparseTensor.__getitem__`): support `np.ndarray` and fix `List[bool]`

support indexing with np.ndarray & fix bug merging from indexing with
List[bool]

* style(tensor, test_tensor): pep8 E501 too long

support indexing with np.ndarray & fix bug merging from indexing
with
List[bool]

* update

* typo

* typo
Co-authored-by: tim <dbwtimteo@outlook.com>
Co-authored-by: rusty1s <matthias.fey@tu-dortmund.de>

test/test_tensor.py

@@ -9,16 +9,50 @@ from .utils import grad_dtypes, devices
 
 @pytest.mark.parametrize('dtype,device', product(grad_dtypes, devices))
 def test_getitem(dtype, device):
-    mat = torch.randn(50, 40, dtype=dtype, device=device)
+    m = 50
+    n = 40
+    k = 10
+    mat = torch.randn(m, n, dtype=dtype, device=device)
     mat = SparseTensor.from_dense(mat)
 
-    idx1 = torch.randint(0, 50, (10, ), dtype=torch.long, device=device)
-    idx2 = torch.randint(0, 40, (10, ), dtype=torch.long, device=device)
+    idx1 = torch.randint(0, m, (k,), dtype=torch.long, device=device)
+    idx2 = torch.randint(0, n, (k,), dtype=torch.long, device=device)
+    bool1 = torch.zeros(m, dtype=torch.bool, device=device)
+    bool2 = torch.zeros(n, dtype=torch.bool, device=device)
+    bool1.scatter_(0, idx1, 1)
+    bool2.scatter_(0, idx2, 1)
+    # idx1 and idx2 may have duplicates
+    k1_bool = bool1.nonzero().size(0)
+    k2_bool = bool2.nonzero().size(0)
 
-    assert mat[:10, :10].sizes() == [10, 10]
-    assert mat[..., :10].sizes() == [50, 10]
-    assert mat[idx1, idx2].sizes() == [10, 10]
-    assert mat[idx1.tolist()].sizes() == [10, 40]
+    idx1np = idx1.cpu().numpy()
+    idx2np = idx2.cpu().numpy()
+    bool1np = bool1.cpu().numpy()
+    bool2np = bool2.cpu().numpy()
+
+    idx1list = idx1np.tolist()
+    idx2list = idx2np.tolist()
+    bool1list = bool1np.tolist()
+    bool2list = bool2np.tolist()
+
+    assert mat[:k, :k].sizes() == [k, k]
+    assert mat[..., :k].sizes() == [m, k]
+
+    assert mat[idx1, idx2].sizes() == [k, k]
+    assert mat[idx1np, idx2np].sizes() == [k, k]
+    assert mat[idx1list, idx2list].sizes() == [k, k]
+
+    assert mat[bool1, bool2].sizes() == [k1_bool, k2_bool]
+    assert mat[bool1np, bool2np].sizes() == [k1_bool, k2_bool]
+    assert mat[bool1list, bool2list].sizes() == [k1_bool, k2_bool]
+
+    assert mat[idx1].sizes() == [k, n]
+    assert mat[idx1np].sizes() == [k, n]
+    assert mat[idx1list].sizes() == [k, n]
+
+    assert mat[bool1].sizes() == [k1_bool, n]
+    assert mat[bool1np].sizes() == [k1_bool, n]
+    assert mat[bool1list].sizes() == [k1_bool, n]
 
 
 @pytest.mark.parametrize('device', devices)

torch_sparse/tensor.py

@@ -2,6 +2,7 @@ from textwrap import indent
 from typing import Optional, List, Tuple, Dict, Union, Any
 
 import torch
+import numpy as np
 import scipy.sparse
 from torch_scatter import segment_csr
 
@@ -468,7 +469,6 @@ def is_shared(self: SparseTensor) -> bool:
 
 def to(self, *args: Optional[List[Any]],
        **kwargs: Optional[Dict[str, Any]]) -> SparseTensor:
-
     device, dtype, non_blocking = torch._C._nn._parse_to(*args, **kwargs)[:3]
 
     if dtype is not None:
@@ -491,7 +491,10 @@ def cuda(self, device: Optional[Union[int, str]] = None,
 def __getitem__(self: SparseTensor, index: Any) -> SparseTensor:
     index = list(index) if isinstance(index, tuple) else [index]
     # More than one `Ellipsis` is not allowed...
-    if len([i for i in index if not torch.is_tensor(i) and i == ...]) > 1:
+    if len([
+            i for i in index
+            if not isinstance(i, (torch.Tensor, np.ndarray)) and i == ...
+    ]) > 1:
         raise SyntaxError
 
     dim = 0
@@ -499,7 +502,10 @@ def __getitem__(self: SparseTensor, index: Any) -> SparseTensor:
     while len(index) > 0:
         item = index.pop(0)
         if isinstance(item, (list, tuple)):
-            item = torch.tensor(item, dtype=torch.long, device=self.device())
+            item = torch.tensor(item, device=self.device())
+        if isinstance(item, np.ndarray):
+            item = torch.from_numpy(item).to(self.device())
+
         if isinstance(item, int):
             out = out.select(dim, item)
             dim += 1